论文标题
机器阅读理解的模块化方法：任务感知专家的混合
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts
论文作者
论文摘要
在这项工作中，我们介绍了一个任务感知专家网络的混合物，用于机器阅读相对较小的数据集上的理解。我们特别关注常识性学习的问题，通过专门培训不同的专家网络来捕捉每个段落，问题和选择三胞胎之间的各种关系，从而实现共同基础知识。此外，我们通过培训每个网络一项相关的专注任务来介绍多任务和转移学习的最新进步。通过实施任务和关系，通过使网络的混合物意识到特定目标，我们可以实现最先进的结果并减少过度拟合。
In this work we present a Mixture of Task-Aware Experts Network for Machine Reading Comprehension on a relatively small dataset. We particularly focus on the issue of common-sense learning, enforcing the common ground knowledge by specifically training different expert networks to capture different kinds of relationships between each passage, question and choice triplet. Moreover, we take inspi ration on the recent advancements of multitask and transfer learning by training each network a relevant focused task. By making the mixture-of-networks aware of a specific goal by enforcing a task and a relationship, we achieve state-of-the-art results and reduce over-fitting.
